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Pharmacogenomics and Bioinformatics in Drug Discovery
October 24, 2018
12:00 PM - 1:00 PM ET

Webinar Description

This lecture will present an overview of various open source databases and servers available that support pharmacogenomics research and drug discovery. Specifically, this lecture will focus on integrated-omics and data-driven approaches for novel drug discovery, drug repositioning, and understanding the molecular basis of drug-induced adverse events. Using two case studies, there will be demonstration of how available data can be repurposed to identify preclinical candidate therapeutics or understand the molecular basis for drug response.

Learning Objectives

  • Introduce and give an overview of some of the state-of-art databases and servers that can be used for pharmacogenomics research and drug discovery
  • Explain how existing data can be harnessed to “guide” or “inform” translational medicine
  • To provide examples of the application of data repurposing for drug discovery

Presenter Bio

jegga-photo-croppedAnil Goud Jegga, DVM, MRes is an Associate Professor in the Division of Biomedical Informatics at Cincinnati Children’s Hospital and Medical Center and the University of Cincinnati. He has more than 18 years of experience in bioinformatics and his research interests are in translational bioinformatics specifically on drug discovery and drug repositioning. The mission of the Jegga Lab is to design, develop and apply novel and robust computational approaches that will accelerate the diffusion of genomics into biomedical research and education and convert the genomics data deluge into systematized knowledge to help us understand the molecular basis of disease. Independently and collaboratively, they have previously developed and published tools that allow biologists with minimal computational experience to integrate diverse data types and synthesize hypotheses about gene and pathway function in human and mouse. His team is currently focusing on developing and implementing systems biology-based novel computational approaches to identify drug candidates for rare lung disorders. They are also working to integrate and mine genomic and compound screening-based big data to identify drug repositioning and novel drug candidates.

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